What is an AI chatbot?

Chatbot vs AI chatbot vs conversational AI

• Chatbot (broadest term): Any program that simulates conversation via text or voice.
• AI chatbot: A chatbot that uses AI (ML/NLP/LLMs) to interpret intent and generate flexible responses.
• Conversational AI: An umbrella term for AI that communicates via text or audio, including both chatbots and voice assistants.

The main types of chatbots

Rule-based / decision-tree

These use if-then rules, menus, and scripted flows. They are predictable and easy to certify but brittle, as they cannot generalize beyond scripted paths. They fit simple FAQs and structured workflows like status checks.

Intent-based ML (classic NLP)

Classifies intent and extracts entities, with a dialog manager selecting the response. Good for constrained domains and customer support with known intents, but requires training data and is limited for open-ended queries.

LLM chatbot (generative)

Uses an LLM to generate responses directly from a prompt or context. Enables natural conversation and handles diverse queries, but carries risks of hallucinations and prompt injection. Often used for knowledge assistance and copilots.

RAG chatbot (LLM + retrieval)

Retrieves relevant documents from a knowledge base then asks an LLM to answer grounded in that retrieved context. This reduces hallucinations and keeps answers up to date. Commonly used for chatting with docs, support, and policy Q&A.

RAG is now a default architecture for enterprise chatbots because it injects current and private knowledge at answer time. Learn more about why RAG is used.

How an AI chatbot works

A typical production AI chatbot pipeline follows these steps:

1. User message comes in: Via web widget, WhatsApp, Slack, in-app chat, or voice.
2. Understanding step: Detects language, intent, and entities; optionally classifies safety.
3. Context + memory: Pulls conversation history and user context.
4. Knowledge step (RAG): Retrieves relevant passages from docs or databases using embeddings and vector search.
5. Generation step: The LLM drafts a response using the retrieved context.
6. Guardrails: Redacts sensitive data and enforces policy.
7. Action/tool calling (optional): Calls APIs if the user asks for a specific action like resetting a password.
8. Handoff to human: Occurs when confidence is low or the user is frustrated.

What AI chatbots are used for

• Customer support: Deflects repetitive tickets and summarizes issues for agents. Learn more about AI for customer support.
• Employee IT/HR helpdesk: Policy Q&A, onboarding, and troubleshooting.
• Sales assistance: Product discovery, lead qualification, and booking meetings.
• In-product copilots: Explains features and guides workflows.

Key limitations and risks

• Hallucinations: Incorrect answers, especially without grounding via RAG.
• Prompt injection: Users tricking the bot into revealing system prompts or private content.
• Security & privacy: Handling PII and model data policies.
• Operational risk: Latency and cost per conversation.

RAG helps reduce factual errors by grounding responses, but shifts the challenge to data quality and retrieval quality.

Practical "good AI chatbot" criteria

• Containment rate: How many issues are solved without an agent.
• Groundedness: Whether answers are supported by retrieved sources.
• Escalation quality: The quality of handoff with summary and metadata.
• Latency and cost: Time-to-first-token and total token usage.
• Safety: Policy adherence and jailbreak resistance.

Summary

An AI chatbot uses natural language processing and machine learning to understand and respond to users, offering a flexible alternative to rigid rule-based systems. By utilizing architectures like RAG, modern AI chatbots can provide accurate, grounded answers for customer support, internal knowledge management, and more.